A method at a computing device for classifying elements within an input, the method including breaking the input into a plurality of patches; for each patch: creating a vector output; applying a characterization map to select a classification bin from a plurality of classification bins; and utilizing the selected classification bin to classify the vector output to create a classified output; and compiling the classified output from each patch.
Legal claims defining the scope of protection, as filed with the USPTO.
2. The method of claim 1, further comprising applying a mask to the image data prior to creating the vector input.
3. The method of claim 1, wherein the creating the vector output is performed using a convolutional neural network.
4. The method of claim 1, wherein each classification bin from the plurality of classification bins contains linear and non-linear classifiers.
5. The method of claim 4, wherein the linear and non-linear classifiers in each classification bin are optimized based on the density map.
6. The method of claim 1, wherein the image data comprises an image of a tumor.
7. The method of claim 6, wherein each classification bin contains linear and non-linear classifiers to identify between different types of cancer cells at a cell density level associated with the classification bin.
8. The method of claim 7, wherein the tumor is a non-small cell lung cancer and the different types of cancer cells include adenocarcinoma and squamous cell carcinoma cells.
9. The method of claim 8, further comprising applying a tumor mask to the image data.
10. The method of claim 7, wherein the tumor is a breast cancer and the different types of cancer cells can be divided into two or more of: Luminal A, Luminal B, Triple-negative/basal-like, and HER2-enriched cells.
12. The computing device of claim 11, wherein the computing device is further configured to utilize a mask for the image data prior to creating the vector input.
13. The computing device of claim 11, wherein the vector output is created using a convolutional neural network.
14. The computing device of claim 11, wherein each classification bin from the plurality of classification bins contains linear and non-linear classifiers.
15. The computing device of claim 11, wherein the linear and non-linear classifiers in each classification bin are optimized based on the density map.
16. The computing device of claim 11, wherein the image data comprises an image of a tumor.
17. The computing device of claim 16, wherein each classification bin contains a support vector machine to identify between different types of cancer cells at a cell density level associated with the classification bin.
18. The computing device of claim 17, wherein the tumor is a non-small cell lung cancer and the different types of cancer cells include adenocarcinoma and squamous cell carcinoma cells.
19. The computing device of claim 18, wherein the processor is further configured to execute instructions to apply a tumor mask to the whole slide image.
20. The computing device of claim 17, wherein the tumor is a breast cancer and the different types of cancer cells can be divided into two or more of: Luminal A, Luminal B, Triple-negative/basal-like, and HER2-enriched cells.
Cooperative Patent Classification codes for this invention. Click any code to explore related patents in that topic.
December 1, 2021
June 13, 2023
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.